Spatial frequency analysis detects altered tissue organization following hamstring strain injury at time of injury but not return to sport

Background Hamstring strain injury (HSI) diagnosis is often corroborated using ultrasound. Spatial frequency analysis (SFA) is a quantitative ultrasound method that has proven useful in characterizing altered tissue organization. The purpose of this study was to determine changes in muscular tissue organization using SFA following HSI. Methods Ultrasound B-mode images were captured at time of injury (TOI) and return to sport (RTS) in collegiate athletes who sustained an HSI. Spatial frequency parameters extracted from two-dimensional Fourier Transforms in user-defined regions of interest (ROI) were analyzed. Separate ROIs encompassed injured and adjacent tissue within the same image of the injured limb and mirrored locations in the contralateral limb at TOI. The ROIs for RTS images were drawn to correspond to the injury-matched location determined from TOI imaging. Peak spatial frequency radius (PSFR) and the fascicular banded pattern relative to image background (Mmax%) were compared between injured and adjacent portions within the same image with separate paired t-tests. Within-image differences of SFA parameters in the injured limb were calculated and compared between TOI and RTS with Wilcoxon rank sum tests. Results Within the injured limb at TOI, PSFR differences in injured and healthy regions did not strictly meet statistical significance (p = 0.06), while Mmax% was different between regions (p < 0.001). No differences were observed between regions in the contralateral limb at TOI (PSFR, p = 0.16; Mmax%, p = 0.30). Significant within-image differences in PSFR (p = 0.03) and Mmax% (p = 0.04) at RTS were detected relative to TOI. Conclusions These findings are a first step in determining the usefulness of SFA in muscle injury characterization and provide quantitative assessment of both fascicular disruption and edema presence in acute HSI.

Spatial-frequency Analysis of the Anatomical Differences in Hamstring Muscles

Spatial frequency analysis (SFA) is a quantitative ultrasound method that characterizes tissue organization. SFA has been used for research involving tendon injury, but may prove useful in similar research involving skeletal muscle. As a first step, we investigated if SFA could detect known architectural differences within hamstring muscles. Ultrasound B-mode images were collected bilaterally at locations corresponding to proximal, mid-belly, and distal thirds along the hamstrings from 10 healthy participants. Images were analyzed in the spatial frequency domain by applying a two-dimensional Fourier Transform in all 6.5 × 6.5 mm kernels in a region of interest corresponding to the central portion of the muscle. SFA parameters (peak spatial frequency radius [PSFR], maximum frequency amplitude [Mmax], sum of frequencies [Sum], and ratio of Mmax to Sum [Mmax%]) were extracted from each muscle location and analyzed by separate linear mixed effects models. Significant differences were observed proximo-distally in PSFR ( p = .039), Mmax ( p < .0001), and Sum ( p < .0001), consistent with architectural descriptions of the hamstring muscles. These results suggest that SFA can detect regional differences of healthy tissue structure within the hamstrings—an important finding for future research in regional muscle structure and mechanics.

Early posterior negativity in humans to pictures of snakes and spiders: effects of proximity

Snakes have proven to drive early attentional capture due to their evolutionary importance, as reflected by the early posterior negativity (EPN). The EPN snake effect might be partly driven by the proximity of the animal. In this study, by employing full-body (medium shot) and head-focused (close-up) pictures, we investigated whether the relative nearness (proximity) of the animal on the picture affects the snake EPN effect. We presented thirty participants with medium shot and close-up snake, spider and bird pictures in a rapid serial presentation paradigm at a presentation rate of three frames per second. We extracted the mean EPN activity from the 225–330 ms time frame after stimulus onset at the parietal–occipital cluster (PO3, O1, Oz, O2, PO4). The results indicate enhanced EPN for snake pictures as compared to spider and bird pictures. In addition, medium-shot snake pictures elicited higher EPN amplitudes than close-up snake pictures, suggesting that the EPN is higher when local, high spatial frequency attributes are visible. Spatial frequency analysis of the stimuli indicated that medium-shot snake pictures possess more power in the high spatial frequency bands, compared to medium-shot spider and bird pictures.

QUANTIFYING DEPTH OF FIELD AND SHARPNESS FOR IMAGE-BASED 3D RECONSTRUCTION OF HERITAGE OBJECTS

Image-based 3D reconstruction processing tools assume sharp focus across the entire object being imaged, but depth of field (DOF) can be a limitation when imaging small to medium sized objects resulting in variation in image sharpness with range from the camera. While DOF is well understood in the context of photographic imaging and it is considered with the acquisition for image-based 3D reconstruction, an “acceptable” level of sharpness and associated “circle of confusion” has not yet been quantified for the 3D case. The work described in this paper contributes to the understanding and quantification of acceptable sharpness by providing evidence of the influence of DOF on the 3D reconstruction of small to medium sized museum objects. Spatial frequency analysis using established collections photography imaging guidelines and targets is used to connect input image quality with 3D reconstruction output quality. Combining quantitative spatial frequency analysis with metrics from a series of comparative 3D reconstructions provides insights into the connection between DOF and output model quality. Lab-based quantification of DOF is used to investigate the influence of sharpness on the output 3D reconstruction to better understand the effects of lens aperture, camera to object surface angle, and taking distance. The outcome provides evidence of the role of DOF in image-based 3D reconstruction and it is briefly presented how masks derived from image content and depth maps can be used to remove unsharp image content and optimise structure from motion (SfM) and multiview stereo (MVS) workflows.